Our ability to live within and interact with a world composed of 3D objects depends largely on our spectacular capacity for visual shape perception. This is what makes vision so critical to our health, happiness, and survival. The long-term goal of this project is to understand 3D object perception by discovering the neural code for complex 3D shape in the primate ventral visual pathway. After decades in which neurophysiological studies of object representation in the monkey ventral pathway have focused exclusively on 2D shape, recent reports indicate a robust representation of 3D shape, although the nature of that representation remains completely unknown. We will address this issue using the same techniques we have recently applied to produce the first quantitative descriptions of complex 2D shape representation. We will combine dense, parametric exploration of 3D shape space with intensive computational analysis to test hypotheses about 3D shape coding dimensions, tuning functions, integration mechanisms, and population coding principles. The stimuli will be complex, smooth (spline-based), abstract, randomly generated 3D shapes. Successive generations of random shape stimuli will be determined with a genetic algorithm, using neural responses as feedback to guide sampling toward the most relevant regions of 3D shape space. The resulting data will be used to test hypotheses about coding dimensions relating to 2D boundary contours, 3D surface patches, and 3D medial axis shape, all described in terms of absolute and relative position, 2D and 3D orientation, 2D and 3D curvature, curvature orientation, and curvature derivative. We will test tuning functions ranging from simple Gaussians to complex manifolds describing highly specific part shapes. We will test a variety of mechanisms for integrating information across object parts, ranging from single-part tuning through multi-part tuning to holistic tuning for overall object shape. The hypotheses surviving from these individual cell analyses will then be tested at the population coding level.